Authors: Mateusz Kuzak, Diana Marek, Hedi Peterson

Disclaimer

We will be using the functions in the ggplot2 package. There are basic plotting capabilities in basic R, but ggplot2 adds more powerful plotting capabilities.

Learning Objectives

  • Visualize some of the mammals data from Figshare surveys.csv
  • Understand how to plot these data using R ggplot2 package. For more details on using ggplot2 see official documentation.
  • Building step by step complex plots with the ggplot2 package

Load required packages

# plotting package
library(ggplot2)
# piping / chaining
library(magrittr)
# modern dataframe manipulations
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> 
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> 
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

Load data directly from figshare.

surveys_raw <- read.csv("http://files.figshare.com/1919744/surveys.csv")

surveys.csv data contains some measurements of the animals caught in plots.

Data cleaning and preparing for plotting

Let’s look at the summary

summary(surveys_raw)

There are few things we need to clean in the dataset.

There are missing values for species_id in some records. Let’s remove those.

surveys_complete <- surveys_raw %>%
                    filter(species_id != "")

We saw in summary, there were NA’s in weight and hindfoot_length. Let’s remove rows with missing values in weight and hindfoot_length. In fact, let’s combine this with removing empty species_id, so we have one command and don’t make lots of intermediate variable names. This is where piping becomes really handy!

surveys_complete <- surveys_raw %>%
                    filter(species_id != "") %>%        # remove missing species_id
                    filter(!is.na(weight)) %>%          # remove missing weight
                    filter(!is.na(hindfoot_length))     # remove missing hindfoot_length

There are a lot of species with low counts, let’s remove the species with less than 10 counts.

# count records per species
species_counts <- surveys_complete %>%
                  group_by(species_id) %>%
                  tally

head(species_counts)

# get names of those frequent species
frequent_species <- species_counts %>%
                    filter(n >= 10) %>%
                    select(species_id)

surveys_complete <- surveys_complete %>%
           filter(species_id %in% frequent_species$species_id)

Make simple scatter plot of hindfoot_length (in millimeters) as a function of weight (in grams), using basic R plotting capabilities.

plot(x = surveys_complete$weight, y = surveys_complete$hindfoot_length)

Plotting with ggplot2

We will make the same plot using the ggplot2 package.

ggplot2 is a plotting package that makes it simple to create complex plots from data in a dataframe. It uses default settings, which help creating publication quality plots with a minimal amount of settings and tweaking.

ggplot graphics are built step by step by adding new elements.

To build a ggplot we need to:

ggplot(data = surveys_complete)
ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length))
ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
  geom_point()

Notes:

Modifying plots

ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
  geom_point(alpha = 0.1)

ggplot(data = surveys_complete, aes(x = weight, y = hindfoot_length)) +
  geom_point(alpha = 0.1, color = "blue")

Boxplot

Visualising the distribution of weight within each species.

ggplot(data = surveys_complete, aes(x = species_id,  y = weight)) +
                   geom_boxplot()

By adding points to boxplot, we can see particular measurements and the abundance of measurements.

ggplot(data = surveys_complete, aes(x = species_id, y = weight)) +
                   geom_jitter(alpha = 0.3, color = "tomato") +
                   geom_boxplot(alpha = 0)

Notice how the boxplot layer is on top of the jitter layer? Play around with the order of geoms and adjust transparency to see how to build up your plot in layers.

Challenge

Create boxplot for hindfoot_length.

Plotting time series data

Let’s calculate number of counts per year for each species. To do that we need to group data first and count records within each group.

yearly_counts <- surveys_complete %>%
                 group_by(year, species_id) %>%
                 tally

Timelapse data can be visualised as a line plot with years on x axis and counts on y axis.

ggplot(data = yearly_counts, aes(x = year, y = n)) +
                  geom_line()

Unfortunately this does not work, because we plot data for all the species together. We need to tell ggplot to split graphed data by species_id

ggplot(data = yearly_counts, aes(x = year, y = n, group = species_id)) +
  geom_line()

We will be able to distinguish species in the plot if we add colors.

ggplot(data = yearly_counts, aes(x = year, y = n, group = species_id, color = species_id)) +
  geom_line()

Faceting

ggplot has a special technique called faceting that allows to split one plot into multiple plots based on some factor. We will use it to plot one time series for each species separately.

ggplot(data = yearly_counts, aes(x = year, y = n, color = species_id)) +
  geom_line() + facet_wrap(~species_id)

Now we would like to split line in each plot by sex of each individual measured. To do that we need to make counts in dataframe grouped by sex.

Challenges:

  • filter the dataframe so that we only keep records with sex “F” or “M”s
sex_values = c("F", "M")
surveys_complete <- surveys_complete %>%
           filter(sex %in% sex_values)
  • group by year, species_id, sex
yearly_sex_counts <- surveys_complete %>%
                     group_by(year, species_id, sex) %>%
                     tally
  • make the faceted plot splitting further by sex (within single plot)
ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = species_id, group = sex)) +
  geom_line() + facet_wrap(~ species_id)

We can improve the plot by coloring by sex instead of species (species are already in separate plots, so we don’t need to distinguish them better)

ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = sex, group = sex)) +
  geom_line() + facet_wrap(~ species_id)

> > - plot average weight of each species over the course of the years

yearly_weight <- surveys_complete %>%
                 group_by(year, species_id, sex) %>%
                 summarise(avg_weight = mean(weight, na.rm = TRUE))
ggplot(data = yearly_weight, aes(x=year, y=avg_weight, color = species_id, group = species_id)) +
  geom_line()

> - why do you think we see those steps in the plot? > - make separate plots per sex since weight of males and females can differ a lot

ggplot(data = yearly_weight, aes(x=year, y=avg_weight, color = species_id, group = species_id)) +
  geom_line() + facet_wrap(~ sex)

So far our result looks quite good, but it is yet far from being publishable. What are other ways one can improve it? Take a look at the ggplot2 cheat sheet (https://www.rstudio.com/wp-content/uploads/2015/08/ggplot2-cheatsheet.pdf), and write down at least three more ideas (can leave them as comments to Etherpad.

Now, let’s change names of axes to something more informative than ‘year’ and ‘n’ and add title to this figure:

ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = sex, group = sex)) +
  geom_line() + 
  facet_wrap(~ species_id) + 
  labs(title = 'Observed species in time', 
       x = 'Year of observation', 
       y = 'Number of species')

Now, thanks to our efforts, axes have much more informative names, yet quite small so it could be hard to read them. Let’s change their size (and font just in sake of fun):

ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = sex, group = sex)) +
  geom_line() + 
  facet_wrap(~ species_id) + 
  labs(title = 'Observed species in time', 
       x = 'Year of observation', 
       y = 'Number of species') + 
  theme(text=element_text(size=16, family="Arial"))

Now, labels became bigger, but there are still few things that one could be improved. Please, take another five minutes and try to add another one or two things, to make it look even more beautiful. Use ggplot2 cheat sheet, which we linked earlier for inspiration.

Here are some ideas from me: